基于DBO算法与BP神经网络相结合的goaf的危险评估
Wentong Wang1, Qianjun Zhang1, Sha Guo1
1School of Environment and Resource, Southwest University of Science and Technology, Mianyang, 621010, China.
Heliyon
|July 29, 2024
概括
一个新的DBO-BP模型有效地评估了goaf危险,在准确性和稳定性方面超过了其他算法. 这种方法为管理与采矿相关的风险和非线性工程问题提供了实际的解决方案.
科学领域:
- 地质工程是地质工程.
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 在中国,由于矿产资源的广泛开采,形形成给经济和发展带来了重大挑战.
- 有效地评估和管理GOAFS对于可持续发展和减轻相关风险至关重要.
研究的目的:
- 介绍和评估 Dung Beetle Optimizer (DBO) 算法的有效性,以评估虫的危害.
- 为了比较DBO-BP模型与其他启发式算法以及反向传播神经网络的性能.
主要方法:
- 开发一个DBO-BP模型,将虫优化器与反向传播神经网络集成在一起.
- 使用粒子优化 (PSO) -BP,鱼优化算法 (WOA) -BP和搜索算法 (SSA) -BP模型进行比较分析.
- 应用模型来评估风危险,包括对矿风的案例研究.
主要成果:
- DBO-BP模型在训练 (至少2.7%更高) 和测试数据集上实现了最高的准确性.
- 与PSO-BP,WOA-BP和SSA-BP模型相比,DBO-BP模型显示出更高的有效性和稳定性.
- 成功验证了DBO-BP模型在现实世界矿goaf危险评估中的可行性.
结论:
- DBO-BP模型提供了一个非常准确和稳定的方法来评估风危险.
- 这项研究为解决采矿和资源管理中的非线性工程问题提供了宝贵的参考.
- 该研究强调了DBO算法在环境和地质风险评估中的潜力.
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